# PaperPod

Local, privacy-first tool that converts a single overhead video recording of
documents (receipts, letters, multi-page stacks) into individual, properly
named PDFs ready for a Paperless-ngx consume folder. All processing runs
offline on your machine.

## How it works

1. Record one continuous overhead video, placing documents under the camera
   one at a time (optionally saying out loud what each one is).
2. PaperPod finds "stable windows" where nothing is moving, picks the sharpest
   frame in each, detects the document outline, and produces a
   perspective-corrected crop.
3. (Upcoming) Spoken descriptions (faster-whisper) or OCR (Tesseract) name
   each document; multi-page stacks are grouped into single PDFs; a review
   step lets you rename/merge/split before export.

## Status

| Module | Purpose | Status |
|---|---|---|
| `capture/` | Frame sampling + audio extraction from video files | Built |
| `vision/` | Motion detection, document contours, perspective crop, sharpness | Built |
| `events/` | State machine: placed / page-flipped / cleared, pod grouping | Planned |
| `transcribe/` | Local speech-to-text (faster-whisper) | Planned |
| `ocr/` | OCR fallback naming (Tesseract) | Planned |
| `naming/` | Final filename assembly + summary CSV | Planned |
| `pdf/` | PDF assembly + Paperless-ngx consume staging | Planned |
| `review_cli/` | Pre-export review (rename/merge/split) | Planned |

## Setup

Requires Python 3.11+ and ffmpeg (`brew install ffmpeg`).

```bash
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

## Usage

```bash
# Generate a synthetic test video (no camera needed)
python sample_data/make_sample_video.py

# Inspect a video
python -m paperpod probe sample_data/videos/synthetic_sample.mp4

# Detect stable windows + document candidates
python -m paperpod detect sample_data/videos/synthetic_sample.mp4

# Extract the audio track (16 kHz mono WAV, whisper-ready)
python -m paperpod extract-audio my_recording.mp4
```

`detect` writes to `output/<video-name>/`:

- `crops/window_NNN.png` — perspective-corrected document candidates
- `frames/window_NNN_full.png` — the full best frame per window (debugging)
- `report.json` — video metadata, motion events, stable windows, detections
- `motion_scores.csv` — per-sample motion scores, for tuning `motion.threshold`

## Tuning

All thresholds live in `config.yaml` (motion sensitivity, stable window
duration, contour size floor, Canny thresholds, speech matching window,
output paths). Plot `motion_scores.csv` to pick a `motion.threshold` that
separates your camera's noise floor from real hand movement.

## Tests

```bash
python -m pytest
```

Tests are self-contained: they synthesize frames and tiny videos on the fly,
no sample assets required.